<xarray.Dataset>
Dimensions: (xt_i: 560, yt_j: 400, xu_i: 560, yu_j: 400, zt_k: 50, lead: 21,
init: 2)
Coordinates:
* xt_i (xt_i) float32 27.5 28.5 29.48 30.46 ... 138.5 139.5 140.5 141.5
* yt_j (yt_j) float32 -34.5 -33.5 -32.52 -31.54 ... 25.75 25.85 25.95
* xu_i (xu_i) float32 28.0 28.99 29.97 30.93 ... 139.0 140.0 141.0 142.0
* yu_j (yu_j) float32 -34.0 -33.01 -32.03 -31.07 ... 25.7 25.8 25.9 26.0
* zt_k (zt_k) float32 1.0 3.017 5.084 ... 4.175e+03 4.731e+03 5.32e+03
* lead (lead) timedelta64[ns] 0 days 00:00:00 ... 5 days 00:00:00
* init (init) datetime64[ns] 2023-06-02 2023-06-03
time (init, lead) datetime64[ns] 2023-06-02 ... 2023-06-08
Data variables:
temp (init, lead, zt_k, yt_j, xt_i) float32 dask.array<chunksize=(1, 21, 50, 400, 560), meta=np.ndarray>
salinity (init, lead, zt_k, yt_j, xt_i) float32 dask.array<chunksize=(1, 21, 50, 400, 560), meta=np.ndarray>
u (init, lead, zt_k, yu_j, xu_i) float32 dask.array<chunksize=(1, 21, 50, 400, 560), meta=np.ndarray>
v (init, lead, zt_k, yu_j, xu_i) float32 dask.array<chunksize=(1, 21, 50, 400, 560), meta=np.ndarray>
hmxl (init, lead, yt_j, xt_i) float32 dask.array<chunksize=(1, 21, 400, 560), meta=np.ndarray>
Attributes:
CDI: Climate Data Interface version 1.7.0 (http://mpimet.mpg....
Conventions: CF-1.4
history: Fri Jun 02 16:25:33 2023: cdo cat -settaxis,2023-06-02,0...
filename: time_mean.000047.06.02.dta.nc
MPP_IO_VERSION: $Id: mpp_io.F90,v 5.3 1999/12/03 16:59:31 vb Exp $
title: Time mean {MOM 3.0}
CDO: Climate Data Operators version 1.7.0 (http://mpimet.mpg.... Dimensions: xt_i : 560yt_j : 400xu_i : 560yu_j : 400zt_k : 50lead : 21init : 2
Coordinates: (8)
xt_i
(xt_i)
float32
27.5 28.5 29.48 ... 140.5 141.5
standard_name : longitude long_name : Longitude of T points units : degrees_E axis : X array([ 27.5 , 28.496922, 29.484686, ..., 139.50385 , 140.50078 ,
141.5 ], dtype=float32) yt_j
(yt_j)
float32
-34.5 -33.5 -32.52 ... 25.85 25.95
standard_name : latitude long_name : Latitude of T points units : degrees_N axis : Y array([-34.5 , -33.50308 , -32.515312, ..., 25.75 , 25.85 ,
25.95 ], dtype=float32) xu_i
(xu_i)
float32
28.0 28.99 29.97 ... 141.0 142.0
standard_name : longitude long_name : Longitude of U points units : degrees_E axis : X array([ 27.99846 , 28.990805, 29.971062, ..., 140.0023 , 141.00038 ,
142. ], dtype=float32) yu_j
(yu_j)
float32
-34.0 -33.01 -32.03 ... 25.9 26.0
standard_name : latitude long_name : Latitude of U points units : degrees_N axis : Y array([-34.001537, -33.009197, -32.02894 , ..., 25.8 , 25.9 ,
26. ], dtype=float32) zt_k
(zt_k)
float32
1.0 3.017 ... 4.731e+03 5.32e+03
long_name : Depth of T grid point units : m positive : down axis : Z array([1.000000e+00, 3.017037e+00, 5.084024e+00, 7.230471e+00, 9.480471e+00,
1.185106e+01, 1.435106e+01, 1.698047e+01, 1.973047e+01, 2.258402e+01,
2.551704e+01, 2.850000e+01, 3.150000e+01, 3.581802e+01, 4.331802e+01,
5.400000e+01, 6.600000e+01, 7.819577e+01, 9.095971e+01, 1.046086e+02,
1.193725e+02, 1.353725e+02, 1.526086e+02, 1.709597e+02, 1.901958e+02,
2.100000e+02, 2.300000e+02, 2.519577e+02, 2.795970e+02, 3.160857e+02,
3.637250e+02, 4.237250e+02, 4.960857e+02, 5.795970e+02, 6.719578e+02,
7.700000e+02, 8.700000e+02, 9.833975e+02, 1.133397e+03, 1.333397e+03,
1.583397e+03, 1.870000e+03, 2.170000e+03, 2.481418e+03, 2.825352e+03,
3.217949e+03, 3.667949e+03, 4.175352e+03, 4.731418e+03, 5.320000e+03],
dtype=float32) lead
(lead)
timedelta64[ns]
0 days 00:00:00 ... 5 days 00:00:00
standard_name : forecast_period array([ 0, 21600000000000, 43200000000000, 64800000000000,
86400000000000, 108000000000000, 129600000000000, 151200000000000,
172800000000000, 194400000000000, 216000000000000, 237600000000000,
259200000000000, 280800000000000, 302400000000000, 324000000000000,
345600000000000, 367200000000000, 388800000000000, 410400000000000,
432000000000000], dtype='timedelta64[ns]') init
(init)
datetime64[ns]
2023-06-02 2023-06-03
standard_name : forecast_reference_time array(['2023-06-02T00:00:00.000000000', '2023-06-03T00:00:00.000000000'],
dtype='datetime64[ns]') time
(init, lead)
datetime64[ns]
2023-06-02 ... 2023-06-08
axis : T standard_name : time array([['2023-06-02T00:00:00.000000000', '2023-06-02T06:00:00.000000000',
'2023-06-02T12:00:00.000000000', '2023-06-02T18:00:00.000000000',
'2023-06-03T00:00:00.000000000', '2023-06-03T06:00:00.000000000',
'2023-06-03T12:00:00.000000000', '2023-06-03T18:00:00.000000000',
'2023-06-04T00:00:00.000000000', '2023-06-04T06:00:00.000000000',
'2023-06-04T12:00:00.000000000', '2023-06-04T18:00:00.000000000',
'2023-06-05T00:00:00.000000000', '2023-06-05T06:00:00.000000000',
'2023-06-05T12:00:00.000000000', '2023-06-05T18:00:00.000000000',
'2023-06-06T00:00:00.000000000', '2023-06-06T06:00:00.000000000',
'2023-06-06T12:00:00.000000000', '2023-06-06T18:00:00.000000000',
'2023-06-07T00:00:00.000000000'],
['2023-06-03T00:00:00.000000000', '2023-06-03T06:00:00.000000000',
'2023-06-03T12:00:00.000000000', '2023-06-03T18:00:00.000000000',
'2023-06-04T00:00:00.000000000', '2023-06-04T06:00:00.000000000',
'2023-06-04T12:00:00.000000000', '2023-06-04T18:00:00.000000000',
'2023-06-05T00:00:00.000000000', '2023-06-05T06:00:00.000000000',
'2023-06-05T12:00:00.000000000', '2023-06-05T18:00:00.000000000',
'2023-06-06T00:00:00.000000000', '2023-06-06T06:00:00.000000000',
'2023-06-06T12:00:00.000000000', '2023-06-06T18:00:00.000000000',
'2023-06-07T00:00:00.000000000', '2023-06-07T06:00:00.000000000',
'2023-06-07T12:00:00.000000000', '2023-06-07T18:00:00.000000000',
'2023-06-08T00:00:00.000000000']], dtype='datetime64[ns]') Data variables: (5)
temp
(init, lead, zt_k, yt_j, xt_i)
float32
dask.array<chunksize=(1, 21, 50, 400, 560), meta=np.ndarray>
long_name : potential temperature units : deg C
Array
Chunk
Bytes
1.75 GiB
897.22 MiB
Shape
(2, 21, 50, 400, 560)
(1, 21, 50, 400, 560)
Dask graph
2 chunks in 7 graph layers
Data type
float32 numpy.ndarray
21
2
560
400
50
salinity
(init, lead, zt_k, yt_j, xt_i)
float32
dask.array<chunksize=(1, 21, 50, 400, 560), meta=np.ndarray>
long_name : salinity units : psu
Array
Chunk
Bytes
1.75 GiB
897.22 MiB
Shape
(2, 21, 50, 400, 560)
(1, 21, 50, 400, 560)
Dask graph
2 chunks in 7 graph layers
Data type
float32 numpy.ndarray
21
2
560
400
50
u
(init, lead, zt_k, yu_j, xu_i)
float32
dask.array<chunksize=(1, 21, 50, 400, 560), meta=np.ndarray>
long_name : Zonal velocity units : cm/s
Array
Chunk
Bytes
1.75 GiB
897.22 MiB
Shape
(2, 21, 50, 400, 560)
(1, 21, 50, 400, 560)
Dask graph
2 chunks in 7 graph layers
Data type
float32 numpy.ndarray
21
2
560
400
50
v
(init, lead, zt_k, yu_j, xu_i)
float32
dask.array<chunksize=(1, 21, 50, 400, 560), meta=np.ndarray>
long_name : Meridional velocity units : cm/s
Array
Chunk
Bytes
1.75 GiB
897.22 MiB
Shape
(2, 21, 50, 400, 560)
(1, 21, 50, 400, 560)
Dask graph
2 chunks in 7 graph layers
Data type
float32 numpy.ndarray
21
2
560
400
50
hmxl
(init, lead, yt_j, xt_i)
float32
dask.array<chunksize=(1, 21, 400, 560), meta=np.ndarray>
long_name : Mixed layer depth units : m
Array
Chunk
Bytes
35.89 MiB
17.94 MiB
Shape
(2, 21, 400, 560)
(1, 21, 400, 560)
Dask graph
2 chunks in 9 graph layers
Data type
float32 numpy.ndarray
2
1
560
400
21
Indexes: (7)
PandasIndex
PandasIndex(Float64Index([ 27.5, 28.49692153930664, 29.48468589782715,
30.45743751525879, 31.409692764282227, 32.33646774291992,
33.23341369628906, 34.0969123840332, 34.92416763305664,
35.71327590942383,
...
132.71376037597656, 133.6538543701172, 134.60610961914062,
135.56927490234375, 136.54202270507812, 137.5229949951172,
138.51075744628906, 139.50384521484375, 140.5007781982422,
141.5],
dtype='float64', name='xt_i', length=560)) PandasIndex
PandasIndex(Float64Index([ -34.5, -33.50307846069336, -32.51531219482422,
-31.54256248474121, -30.590307235717773, -29.663530349731445,
-28.766584396362305, -27.903087615966797, -27.07583236694336,
-26.286724090576172,
...
25.049999237060547, 25.149999618530273, 25.25,
25.350000381469727, 25.450000762939453, 25.549999237060547,
25.649999618530273, 25.75, 25.850000381469727,
25.950000762939453],
dtype='float64', name='yt_j', length=400)) PandasIndex
PandasIndex(Float64Index([ 27.99846076965332, 28.99080467224121, 29.97106170654297,
30.933565139770508, 31.87308120727539, 32.784942626953125,
33.665164947509766, 34.51054000854492, 35.318721771240234,
36.08827590942383,
...
133.18380737304688, 134.12998962402344, 135.0876922607422,
136.05564880371094, 137.03250122070312, 138.01687622070312,
139.00730895996094, 140.00230407714844, 141.00038146972656,
142.0],
dtype='float64', name='xu_i', length=560)) PandasIndex
PandasIndex(Float64Index([ -34.00153732299805, -33.00919723510742, -32.02893829345703,
-31.066434860229492, -30.12691879272461, -29.215057373046875,
-28.334836959838867, -27.489459991455078, -26.681278228759766,
-25.911724090576172,
...
25.100000381469727, 25.200000762939453, 25.299999237060547,
25.399999618530273, 25.5, 25.600000381469727,
25.700000762939453, 25.799999237060547, 25.899999618530273,
26.0],
dtype='float64', name='yu_j', length=400)) PandasIndex
PandasIndex(Float64Index([ 1.0, 3.0170371532440186, 5.084024429321289,
7.230471134185791, 9.480470657348633, 11.851061820983887,
14.351061820983887, 16.980470657348633, 19.730470657348633,
22.58402442932129, 25.51703643798828, 28.5,
31.5, 35.81801986694336, 43.31801986694336,
54.0, 66.0, 78.19577026367188,
90.95970916748047, 104.60856628417969, 119.37249755859375,
135.37249755859375, 152.6085662841797, 170.95970153808594,
190.19577026367188, 210.0, 230.0,
251.95773315429688, 279.5970458984375, 316.0856628417969,
363.7249755859375, 423.7249755859375, 496.0856628417969,
579.5970458984375, 671.957763671875, 770.0,
870.0, 983.3974609375, 1133.3974609375,
1333.3974609375, 1583.3974609375, 1870.0,
2170.0, 2481.41796875, 2825.35205078125,
3217.949462890625, 3667.949462890625, 4175.35205078125,
4731.41796875, 5320.0],
dtype='float64', name='zt_k')) PandasIndex
PandasIndex(TimedeltaIndex(['0 days 00:00:00', '0 days 06:00:00', '0 days 12:00:00',
'0 days 18:00:00', '1 days 00:00:00', '1 days 06:00:00',
'1 days 12:00:00', '1 days 18:00:00', '2 days 00:00:00',
'2 days 06:00:00', '2 days 12:00:00', '2 days 18:00:00',
'3 days 00:00:00', '3 days 06:00:00', '3 days 12:00:00',
'3 days 18:00:00', '4 days 00:00:00', '4 days 06:00:00',
'4 days 12:00:00', '4 days 18:00:00', '5 days 00:00:00'],
dtype='timedelta64[ns]', name='lead', freq=None)) PandasIndex
PandasIndex(DatetimeIndex(['2023-06-02', '2023-06-03'], dtype='datetime64[ns]', name='init', freq=None)) Attributes: (7)
CDI : Climate Data Interface version 1.7.0 (http://mpimet.mpg.de/cdi) Conventions : CF-1.4 history : Fri Jun 02 16:25:33 2023: cdo cat -settaxis,2023-06-02,00:00:00,6hour -selvar,temp,salinity,u,v,hmxl /nas/oceanopr/BENCH/test_anal/WORK/REST/NC/time202306020000.nc -settaxis,2023-06-02,06:00:00,6hour -selvar,temp,salinity,u,v,hmxl /nas/oceanopr/BENCH/test_forc/WORK/REST/NC/time202306020000.nc SAC_OSF_CIRC_10KM_20230602.nc filename : time_mean.000047.06.02.dta.nc MPP_IO_VERSION : $Id: mpp_io.F90,v 5.3 1999/12/03 16:59:31 vb Exp $ title : Time mean {MOM 3.0} CDO : Climate Data Operators version 1.7.0 (http://mpimet.mpg.de/cdo)